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One of the most important skills for a data analyst is proficiency in a programming language. Data analysts use SQL (Structured Query Language) to communicate with databases, but when it comes to cleaning, manipulating, analyzing, and visualizing data, you鈥檙e looking at either Python or R.

In this article, we'll explore how Python and R are used for data analysis, including how they differ from one another, how to choose the right one for you, and ways to learn them.听

When you鈥檙e ready to start learning a programming language, consider enrolling in the IBM Data Analytics With Excel and R Professional Certificate. You鈥檒l have the opportunity to learn how to conduct data analysis using R in as little as three months. Upon completion, you鈥檒l have earned a career credential that demonstrates your expertise.

Python vs. R for data analysis: What鈥檚 the difference?聽

Python and R are both free, open-source languages that can run on Windows, macOS, and Linux. Both can handle a wide range of data analysis tasks, and both are considered relatively easy languages to learn, especially for beginners. So, which should you choose to learn (or learn first)? Before we dig into the differences, here鈥檚 a broad overview of each language.听

What is Python?

Python is a high-level, general-purpose programming language known for its intuitive syntax that mimics natural language. You can use Python code for a wide variety of tasks, but three popular applications include:

• Data science and data analysis

• Web application development

• Automation/scripting

What does 鈥渉igh-level鈥� mean?

A high-level programming language features a syntax that is easy for humans to read and understand. Low-level languages are those that can be easily understood by a machine. Examples of high-level languages include Python, C++, C#, and Java.

When you write code in a high-level language, it converts into a low-level language, or machine code, that your computer can recognize and run.

Read more: Low-Level vs. High-Level Programming Languages

What is R?

R is a software environment and statistical programming language built for statistical computing and data visualization. R鈥檚 numerous abilities tend to fall into three broad categories:

• Manipulating data

• Statistical analysis

• Visualizing data聽

Hear more about what R can do from Carrie, a data analyst at Google, in this lecture from Google's Data Analytics Professional Certificate:

How do I choose between Python and R?

There鈥檚 no wrong choice when it comes to learning Python or R. Both are in-demand skills and will allow you to perform just about any data analytics task you鈥檒l encounter. Which one is better for you will ultimately come down to your background, interests, and career goals.听

As you make your decision, here are some things to consider.

1. Learning curve

Both Python and R are considered fairly easy languages to learn. Python was originally designed for software development. If you have previous experience with Java or C++,聽you may be able to pick up Python more naturally than R. If you have a background in statistics, on the other hand, R could be a bit easier.

Overall, Python鈥檚 readable syntax gives it a smoother learning curve. R tends to have a steep learning curve at the beginning, but once you understand how to use its features, it gets significantly easier.听 聽

Tip: Once you鈥檝e learned one programming language, it鈥檚 typically easier to learn another one.听

2. Company

In general, it鈥檚 a good idea to 鈥渟peak鈥� the same language as the team with which you鈥檒l be working. This makes it easier to share code and collaborate on projects.听

If you鈥檙e just starting out, you may not know what company you鈥檒l eventually work for. Take a look at a few job listings for the companies and industries you鈥檙e most interested in. Do they tend to list R or Python as a requirement? This could be a good indication of which direction to take your learning.

3. R vs. Python: Strengths and weaknesses

While both Python and R can accomplish many of the same data tasks, they each have their own unique strengths. If you know you鈥檒l be spending lots of time on certain data tasks, you might want to prioritize the language that excels at those tasks.

4. Career path

Think about how learning a programming language fits in with your longer-term career goals. If you鈥檙e passionate about the statistical calculation and data visualization portions of data analysis, R could be a good fit for you.

If, on the other hand, you鈥檙e interested in becoming a data scientist and working with big data, artificial intelligence, and deep learning algorithms, Python would be the better fit.听

The same is true if your personal or professional interests extend beyond data and into programming, development, or other computer science fields. Python is a general-purpose language used for a much wider range of tasks than R.

Python and R: By the numbers

According to several popular programming language indices, TIOBE [], Stack Overflow [], PYPL [], and RedMonk [], Python is far and away one of the more popular languages across the broader tech community.听

While this doesn鈥檛 necessarily mean it鈥檚 better, it does suggest that it鈥檚 more widely used and may have a more robust community for ongoing support and development.

How to learn R or Python: Options to get started

Python and R are both excellent languages for data. They鈥檙e also both appropriate for beginners with no previous coding experience. Luckily, no matter which language you choose to pursue first, you鈥檒l find a wide range of resources and materials to help you along the way. These are just a few options for getting started.

Try both through Guided Projects.

Another great way to decide whether to learn R or Python is to try them both out. 糖心vlog官网观看鈥檚 Guided Projects offer a hands-on introduction in under two hours without having to buy or download any software.听

With Getting Started with R, you can start writing basic R commands and learn how to install packages and import data sets. With Introduction to Python, which takes under an hour to finish, you can write a guessing game application as you learn to create variables, decision constructs, and loops.

Practice your Python skills independently with tutorials

Not ready to commit to a course or a boot camp yet? You can read step-by-step guides for troubleshooting Python basics like syntax, if-else statements, exceptions, and working with loops in 糖心vlog官网观看's free programming tutorials.

Take a course.

If you prefer focusing on one skill at a time (or if you鈥檙e adding a new coding language to your existing data analyst skill set), a course in Python or R could get you started. There are a ton of classes out there to choose from. On 糖心vlog官网观看, the most popular options among learners are Programming for Everybody (Getting Started with Python) from the University of Michigan and R Programming from Johns Hopkins University.

Tip: For many learners, it may be better to pick one language and get proficient rather than trying to learn both at the same time.听

Further explore R, Python, and data analysis with free resources

Considering a career change? Join on LinkedIn to keep up with programming trends and data analysis job opportunities. As you learn about R and Python for data analysis, consider these helpful resources:

• Broaden your knowledge: Data Analysis Terms and Definitions

• Learn more about Python: Python Syntax Cheat Sheet

• Hear from an expert: 7 Questions With a Data Analytics Professor

With 糖心vlog官网观看 Plus, you can learn and earn credentials at your own pace from over 350 leading companies and universities. With a monthly or annual subscription, you鈥檒l gain access to over 10,000 programs鈥攋ust check the course page to confirm your selection is included.

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